Overview

Dataset statistics

Number of variables11
Number of observations105987
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory31.9 MiB
Average record size in memory315.8 B

Variable types

Text3
Numeric7
DateTime1

Alerts

dom_basis is highly overall correlated with dom_basis_rate and 2 other fieldsHigh correlation
dom_basis_rate is highly overall correlated with dom_basis and 2 other fieldsHigh correlation
dominant_contract_price is highly overall correlated with near_contract_price and 1 other fieldsHigh correlation
near_basis is highly overall correlated with dom_basis and 2 other fieldsHigh correlation
near_basis_rate is highly overall correlated with dom_basis and 2 other fieldsHigh correlation
near_contract_price is highly overall correlated with dominant_contract_price and 1 other fieldsHigh correlation
spot_price is highly overall correlated with dominant_contract_price and 1 other fieldsHigh correlation

Reproduction

Analysis started2024-03-21 02:18:50.716177
Analysis finished2024-03-21 02:19:06.994781
Duration16.28 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.9 MiB
2024-03-21T10:19:07.602403image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.7968147
Min length1

Characters and Unicode

Total characters190439
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowA
3rd rowM
4th rowY
5th rowP
ValueCountFrequency (%)
c 2715
 
2.6%
y 2715
 
2.6%
p 2715
 
2.6%
l 2715
 
2.6%
v 2715
 
2.6%
a 2715
 
2.6%
m 2715
 
2.6%
au 2714
 
2.6%
cu 2714
 
2.6%
zn 2714
 
2.6%
Other values (41) 78840
74.4%
2024-03-21T10:19:08.419133image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 19022
 
10.0%
R 17385
 
9.1%
A 16850
 
8.8%
P 16092
 
8.4%
C 14542
 
7.6%
U 14486
 
7.6%
M 12819
 
6.7%
F 11271
 
5.9%
B 9191
 
4.8%
G 7657
 
4.0%
Other values (13) 51124
26.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 190439
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 19022
 
10.0%
R 17385
 
9.1%
A 16850
 
8.8%
P 16092
 
8.4%
C 14542
 
7.6%
U 14486
 
7.6%
M 12819
 
6.7%
F 11271
 
5.9%
B 9191
 
4.8%
G 7657
 
4.0%
Other values (13) 51124
26.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 190439
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 19022
 
10.0%
R 17385
 
9.1%
A 16850
 
8.8%
P 16092
 
8.4%
C 14542
 
7.6%
U 14486
 
7.6%
M 12819
 
6.7%
F 11271
 
5.9%
B 9191
 
4.8%
G 7657
 
4.0%
Other values (13) 51124
26.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 190439
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 19022
 
10.0%
R 17385
 
9.1%
A 16850
 
8.8%
P 16092
 
8.4%
C 14542
 
7.6%
U 14486
 
7.6%
M 12819
 
6.7%
F 11271
 
5.9%
B 9191
 
4.8%
G 7657
 
4.0%
Other values (13) 51124
26.8%

spot_price
Real number (ℝ)

HIGH CORRELATION 

Distinct32537
Distinct (%)30.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13688.09
Minimum11.2
Maximum368475
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size828.1 KiB
2024-03-21T10:19:08.602639image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum11.2
5-th percentile840
Q13063.12
median5278
Q39604.585
95-th percentile57831
Maximum368475
Range368463.8
Interquartile range (IQR)6541.465

Descriptive statistics

Standard deviation31342.95
Coefficient of variation (CV)2.2897972
Kurtosis30.731529
Mean13688.09
Median Absolute Deviation (MAD)2688
Skewness5.112544
Sum1.4507596 × 109
Variance9.8238054 × 108
MonotonicityNot monotonic
2024-03-21T10:19:08.808280image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5100 190
 
0.2%
4400 117
 
0.1%
5400 105
 
0.1%
5200 92
 
0.1%
4600 91
 
0.1%
3750 91
 
0.1%
5600 85
 
0.1%
2433.33 85
 
0.1%
5300 83
 
0.1%
5500 82
 
0.1%
Other values (32527) 104966
99.0%
ValueCountFrequency (%)
11.2 1
< 0.1%
11.28 2
< 0.1%
11.33 1
< 0.1%
11.5 1
< 0.1%
11.62 1
< 0.1%
11.63 1
< 0.1%
11.8 1
< 0.1%
11.87 1
< 0.1%
11.9 2
< 0.1%
11.92 1
< 0.1%
ValueCountFrequency (%)
368475 1
< 0.1%
355162.5 1
< 0.1%
351350 1
< 0.1%
351225 1
< 0.1%
350975 1
< 0.1%
350350 1
< 0.1%
350162.5 1
< 0.1%
349537.5 2
< 0.1%
347912.5 2
< 0.1%
347350 1
< 0.1%
Distinct4370
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size6.3 MiB
2024-03-21T10:19:10.009776image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.476917
Min length4

Characters and Unicode

Total characters580482
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)< 0.1%

Sample

1st rowc1301
2nd rowa1301
3rd rowm1301
4th rowy1301
5th rowp1301
ValueCountFrequency (%)
ma506 244
 
0.2%
rs407 239
 
0.2%
lh2109 175
 
0.2%
rs607 163
 
0.2%
oi307 163
 
0.2%
rs507 162
 
0.2%
rs107 162
 
0.2%
rs307 162
 
0.2%
rs807 162
 
0.2%
rs707 161
 
0.2%
Other values (4319) 104194
98.3%
2024-03-21T10:19:11.470754image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 99186
17.1%
1 96630
16.6%
2 56003
 
9.6%
3 27728
 
4.8%
7 21406
 
3.7%
9 20933
 
3.6%
5 19383
 
3.3%
4 17102
 
2.9%
8 16401
 
2.8%
6 15271
 
2.6%
Other values (37) 190439
32.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 580482
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 99186
17.1%
1 96630
16.6%
2 56003
 
9.6%
3 27728
 
4.8%
7 21406
 
3.7%
9 20933
 
3.6%
5 19383
 
3.3%
4 17102
 
2.9%
8 16401
 
2.8%
6 15271
 
2.6%
Other values (37) 190439
32.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 580482
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 99186
17.1%
1 96630
16.6%
2 56003
 
9.6%
3 27728
 
4.8%
7 21406
 
3.7%
9 20933
 
3.6%
5 19383
 
3.3%
4 17102
 
2.9%
8 16401
 
2.8%
6 15271
 
2.6%
Other values (37) 190439
32.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 580482
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 99186
17.1%
1 96630
16.6%
2 56003
 
9.6%
3 27728
 
4.8%
7 21406
 
3.7%
9 20933
 
3.6%
5 19383
 
3.3%
4 17102
 
2.9%
8 16401
 
2.8%
6 15271
 
2.6%
Other values (37) 190439
32.8%

near_contract_price
Real number (ℝ)

HIGH CORRELATION 

Distinct19681
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13547.08
Minimum0
Maximum370200
Zeros19
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size828.1 KiB
2024-03-21T10:19:11.661181image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1010
Q12940
median5206
Q39922
95-th percentile57767
Maximum370200
Range370200
Interquartile range (IQR)6982

Descriptive statistics

Standard deviation30846.682
Coefficient of variation (CV)2.2769986
Kurtosis30.530684
Mean13547.08
Median Absolute Deviation (MAD)2670
Skewness5.0916011
Sum1.4358143 × 109
Variance9.5151778 × 108
MonotonicityNot monotonic
2024-03-21T10:19:11.858635image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3122 284
 
0.3%
3198 135
 
0.1%
3317 132
 
0.1%
2507 99
 
0.1%
2309 97
 
0.1%
2350 96
 
0.1%
4800 95
 
0.1%
3121 93
 
0.1%
2340 81
 
0.1%
2500 76
 
0.1%
Other values (19671) 104799
98.9%
ValueCountFrequency (%)
0 19
< 0.1%
214.95 2
 
< 0.1%
215.95 1
 
< 0.1%
216 2
 
< 0.1%
216.4 1
 
< 0.1%
216.55 1
 
< 0.1%
217.1 1
 
< 0.1%
217.3 1
 
< 0.1%
217.5 1
 
< 0.1%
217.8 1
 
< 0.1%
ValueCountFrequency (%)
370200 1
< 0.1%
355130 1
< 0.1%
352990 1
< 0.1%
349700 1
< 0.1%
349200 1
< 0.1%
348350 1
< 0.1%
348290 1
< 0.1%
347860 1
< 0.1%
347770 1
< 0.1%
346050 1
< 0.1%
Distinct2066
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size6.3 MiB
2024-03-21T10:19:12.876536image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.4768792
Min length4

Characters and Unicode

Total characters580478
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)< 0.1%

Sample

1st rowc1305
2nd rowa1305
3rd rowm1305
4th rowy1305
5th rowp1305
ValueCountFrequency (%)
rs409 245
 
0.2%
pm007 240
 
0.2%
rs311 223
 
0.2%
ma506 203
 
0.2%
rs607 201
 
0.2%
wh303 200
 
0.2%
rs909 176
 
0.2%
wr1504 176
 
0.2%
rs507 176
 
0.2%
ta401 175
 
0.2%
Other values (2023) 103972
98.1%
2024-03-21T10:19:14.081357image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 108900
18.8%
1 89970
15.5%
2 52340
 
9.0%
9 34867
 
6.0%
5 33972
 
5.9%
3 17698
 
3.0%
4 14651
 
2.5%
6 14182
 
2.4%
u 13381
 
2.3%
p 13265
 
2.3%
Other values (37) 187252
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 580478
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 108900
18.8%
1 89970
15.5%
2 52340
 
9.0%
9 34867
 
6.0%
5 33972
 
5.9%
3 17698
 
3.0%
4 14651
 
2.5%
6 14182
 
2.4%
u 13381
 
2.3%
p 13265
 
2.3%
Other values (37) 187252
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 580478
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 108900
18.8%
1 89970
15.5%
2 52340
 
9.0%
9 34867
 
6.0%
5 33972
 
5.9%
3 17698
 
3.0%
4 14651
 
2.5%
6 14182
 
2.4%
u 13381
 
2.3%
p 13265
 
2.3%
Other values (37) 187252
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 580478
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 108900
18.8%
1 89970
15.5%
2 52340
 
9.0%
9 34867
 
6.0%
5 33972
 
5.9%
3 17698
 
3.0%
4 14651
 
2.5%
6 14182
 
2.4%
u 13381
 
2.3%
p 13265
 
2.3%
Other values (37) 187252
32.3%

dominant_contract_price
Real number (ℝ)

HIGH CORRELATION 

Distinct20889
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13536.419
Minimum0
Maximum371700
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size828.1 KiB
2024-03-21T10:19:14.293533image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile938
Q12952
median5180
Q39710
95-th percentile57984
Maximum371700
Range371700
Interquartile range (IQR)6758

Descriptive statistics

Standard deviation30760.708
Coefficient of variation (CV)2.2724405
Kurtosis29.895941
Mean13536.419
Median Absolute Deviation (MAD)2626
Skewness5.0489894
Sum1.4346845 × 109
Variance9.4622113 × 108
MonotonicityNot monotonic
2024-03-21T10:19:14.496953image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3122 411
 
0.4%
3198 269
 
0.3%
3748 172
 
0.2%
3121 171
 
0.2%
2512 103
 
0.1%
2230 88
 
0.1%
2676 87
 
0.1%
2550 80
 
0.1%
2330 71
 
0.1%
2514 71
 
0.1%
Other values (20879) 104464
98.6%
ValueCountFrequency (%)
0 12
< 0.1%
218.75 1
 
< 0.1%
219.25 1
 
< 0.1%
219.45 2
 
< 0.1%
219.6 1
 
< 0.1%
219.7 1
 
< 0.1%
219.95 1
 
< 0.1%
220.3 1
 
< 0.1%
220.6 1
 
< 0.1%
220.75 1
 
< 0.1%
ValueCountFrequency (%)
371700 1
< 0.1%
355630 1
< 0.1%
352500 1
< 0.1%
349470 1
< 0.1%
346340 1
< 0.1%
345950 1
< 0.1%
345820 1
< 0.1%
345770 1
< 0.1%
344010 1
< 0.1%
342690 1
< 0.1%

near_basis
Real number (ℝ)

HIGH CORRELATION 

Distinct25187
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141.01023
Minimum-29072.03
Maximum93450
Zeros342
Zeros (%)0.3%
Negative34901
Negative (%)32.9%
Memory size828.1 KiB
2024-03-21T10:19:14.694877image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-29072.03
5-th percentile-420
Q1-32
median77.67
Q3284
95-th percentile1470
Maximum93450
Range122522.03
Interquartile range (IQR)316

Descriptive statistics

Standard deviation2105.0262
Coefficient of variation (CV)14.928181
Kurtosis278.67538
Mean141.01023
Median Absolute Deviation (MAD)147.67
Skewness2.9118818
Sum14945251
Variance4431135.3
MonotonicityNot monotonic
2024-03-21T10:19:14.874450image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 342
 
0.3%
10 304
 
0.3%
20 292
 
0.3%
50 273
 
0.3%
100 260
 
0.2%
-10 260
 
0.2%
40 254
 
0.2%
60 241
 
0.2%
70 239
 
0.2%
90 239
 
0.2%
Other values (25177) 103283
97.4%
ValueCountFrequency (%)
-29072.03 1
< 0.1%
-28956.9 1
< 0.1%
-28881.32 1
< 0.1%
-28847.28 1
< 0.1%
-28831.55 1
< 0.1%
-28802.1 1
< 0.1%
-28736.32 1
< 0.1%
-28725.7 1
< 0.1%
-28671.97 1
< 0.1%
-28606.3 1
< 0.1%
ValueCountFrequency (%)
93450 1
< 0.1%
83900 1
< 0.1%
82550 1
< 0.1%
72950 1
< 0.1%
70700 1
< 0.1%
70550 1
< 0.1%
67400 1
< 0.1%
65550 1
< 0.1%
62700 1
< 0.1%
61000 1
< 0.1%

dom_basis
Real number (ℝ)

HIGH CORRELATION 

Distinct27707
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151.6708
Minimum-29072.03
Maximum93450
Zeros204
Zeros (%)0.2%
Negative39263
Negative (%)37.0%
Memory size828.1 KiB
2024-03-21T10:19:15.056356image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-29072.03
5-th percentile-536
Q1-65.67
median79
Q3343
95-th percentile1540
Maximum93450
Range122522.03
Interquartile range (IQR)408.67

Descriptive statistics

Standard deviation2189.5453
Coefficient of variation (CV)14.436169
Kurtosis239.45275
Mean151.6708
Median Absolute Deviation (MAD)188.33
Skewness2.3285325
Sum16075133
Variance4794108.8
MonotonicityNot monotonic
2024-03-21T10:19:15.485175image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 204
 
0.2%
10 191
 
0.2%
20 182
 
0.2%
90 182
 
0.2%
140 182
 
0.2%
-10 179
 
0.2%
-40 177
 
0.2%
50 175
 
0.2%
40 174
 
0.2%
-30 170
 
0.2%
Other values (27697) 104171
98.3%
ValueCountFrequency (%)
-29072.03 1
< 0.1%
-28956.9 1
< 0.1%
-28881.32 1
< 0.1%
-28847.28 1
< 0.1%
-28831.55 1
< 0.1%
-28802.1 1
< 0.1%
-28736.32 1
< 0.1%
-28725.7 1
< 0.1%
-28671.97 1
< 0.1%
-28606.3 1
< 0.1%
ValueCountFrequency (%)
93450 1
< 0.1%
83900 1
< 0.1%
82550 1
< 0.1%
72950 1
< 0.1%
70700 1
< 0.1%
70550 1
< 0.1%
67400 1
< 0.1%
65550 1
< 0.1%
62700 1
< 0.1%
61000 1
< 0.1%

near_basis_rate
Real number (ℝ)

HIGH CORRELATION 

Distinct89448
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.1464508
Minimum-1410.5414
Maximum1
Zeros342
Zeros (%)0.3%
Negative34901
Negative (%)32.9%
Memory size828.1 KiB
2024-03-21T10:19:15.664340image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-1410.5414
5-th percentile-0.080362392
Q1-0.005935564
median0.012048193
Q30.045703488
95-th percentile0.14871389
Maximum1
Range1411.5414
Interquartile range (IQR)0.051639052

Descriptive statistics

Standard deviation84.494673
Coefficient of variation (CV)-11.823306
Kurtosis140.4337
Mean-7.1464508
Median Absolute Deviation (MAD)0.025112096
Skewness-11.852332
Sum-757430.88
Variance7139.3497
MonotonicityNot monotonic
2024-03-21T10:19:15.854438image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 342
 
0.3%
0.02857142857 30
 
< 0.1%
0.04 22
 
< 0.1%
1 19
 
< 0.1%
0.01428571429 18
 
< 0.1%
0.006984627116 18
 
< 0.1%
0.025 17
 
< 0.1%
-0.05 17
 
< 0.1%
-0.337254902 17
 
< 0.1%
0.02 17
 
< 0.1%
Other values (89438) 105470
99.5%
ValueCountFrequency (%)
-1410.541382 1
< 0.1%
-1382.076923 1
< 0.1%
-1356.307692 1
< 0.1%
-1333.399431 1
< 0.1%
-1329.666667 1
< 0.1%
-1327.33217 1
< 0.1%
-1311.785388 1
< 0.1%
-1302.767123 1
< 0.1%
-1302.664921 1
< 0.1%
-1301.903226 1
< 0.1%
ValueCountFrequency (%)
1 19
< 0.1%
0.7216438356 1
 
< 0.1%
0.7087912088 1
 
< 0.1%
0.6821917808 1
 
< 0.1%
0.681043956 1
 
< 0.1%
0.6777353082 1
 
< 0.1%
0.6765136719 1
 
< 0.1%
0.6696082652 1
 
< 0.1%
0.6679616088 1
 
< 0.1%
0.6627326373 1
 
< 0.1%

dom_basis_rate
Real number (ℝ)

HIGH CORRELATION 

Distinct96656
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.5567127
Minimum-1420.8382
Maximum1
Zeros204
Zeros (%)0.2%
Negative39263
Negative (%)37.0%
Memory size828.1 KiB
2024-03-21T10:19:16.035863image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-1420.8382
5-th percentile-0.08535405
Q1-0.011368997
median0.012995006
Q30.055152055
95-th percentile0.17506269
Maximum1
Range1421.8382
Interquartile range (IQR)0.066521052

Descriptive statistics

Standard deviation89.548429
Coefficient of variation (CV)-11.850183
Kurtosis141.97501
Mean-7.5567127
Median Absolute Deviation (MAD)0.031764188
Skewness-11.909688
Sum-800913.31
Variance8018.9211
MonotonicityNot monotonic
2024-03-21T10:19:16.238750image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 204
 
0.2%
0.1 20
 
< 0.1%
0.03333333333 20
 
< 0.1%
-0.0731372549 17
 
< 0.1%
0.02 17
 
< 0.1%
0.08995983936 16
 
< 0.1%
0.0661641541 16
 
< 0.1%
0.03661202186 15
 
< 0.1%
0.05433057555 15
 
< 0.1%
0.07660961695 14
 
< 0.1%
Other values (96646) 105633
99.7%
ValueCountFrequency (%)
-1420.838178 1
< 0.1%
-1410.541382 1
< 0.1%
-1394.2 1
< 0.1%
-1385.590038 1
< 0.1%
-1382.076923 1
< 0.1%
-1358.042553 1
< 0.1%
-1356.307692 1
< 0.1%
-1350.012146 1
< 0.1%
-1333.767642 1
< 0.1%
-1333.399431 1
< 0.1%
ValueCountFrequency (%)
1 12
< 0.1%
0.6700907788 1
 
< 0.1%
0.658820841 1
 
< 0.1%
0.6579589844 1
 
< 0.1%
0.6571244081 1
 
< 0.1%
0.6504766228 1
 
< 0.1%
0.6503656307 1
 
< 0.1%
0.6494169096 1
 
< 0.1%
0.6485374771 1
 
< 0.1%
0.6453170937 1
 
< 0.1%

date
Date

Distinct2715
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size828.1 KiB
Minimum2013-01-04 00:00:00
Maximum2024-03-11 00:00:00
2024-03-21T10:19:16.434687image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:16.647890image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-03-21T10:19:05.113064image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:18:53.654777image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:18:55.458663image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:18:57.189777image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:00.382930image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:02.289381image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:03.714803image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:05.271482image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:18:53.849406image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:18:55.690337image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:18:57.477921image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:00.575196image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:02.509452image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:03.912169image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:05.434529image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:18:54.046666image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:18:55.930445image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:18:57.732747image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:00.854743image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:02.718582image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:04.102535image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:05.620238image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:18:54.282339image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:18:56.169247image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:18:58.002012image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:01.296710image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:02.927906image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:04.301876image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:05.786641image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:18:54.729385image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:18:56.356114image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:18:58.187912image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:01.632284image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:03.119461image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:04.518931image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:05.938398image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:18:54.976764image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:18:56.546770image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:18:58.383748image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:01.850625image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:03.318890image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:04.715119image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:06.117082image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:18:55.223970image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:18:56.779796image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:18:58.573023image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:02.075206image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:03.513274image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:19:04.924884image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2024-03-21T10:19:16.790643image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
dom_basisdom_basis_ratedominant_contract_pricenear_basisnear_basis_ratenear_contract_pricespot_price
dom_basis1.0000.9000.1090.6910.6160.1370.237
dom_basis_rate0.9001.000-0.1100.6090.668-0.0790.028
dominant_contract_price0.109-0.1101.0000.162-0.0960.9970.956
near_basis0.6910.6090.1621.0000.8960.1500.268
near_basis_rate0.6160.668-0.0960.8961.000-0.1080.017
near_contract_price0.137-0.0790.9970.150-0.1081.0000.958
spot_price0.2370.0280.9560.2680.0170.9581.000

Missing values

2024-03-21T10:19:06.351153image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-21T10:19:06.677765image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

varietyspot_pricenear_contractnear_contract_pricedominant_contractdominant_contract_pricenear_basisdom_basisnear_basis_ratedom_basis_ratedate
0C2330.45c13012330.0c13052443.00.45-112.550.000193-0.0482952013-01-04 00:00:00
1A4520.00a13014590.0a13054730.0-70.00-210.00-0.015487-0.0464602013-01-04 00:00:00
2M3963.00m13013813.0m13053241.0150.00722.000.0378500.1821852013-01-04 00:00:00
3Y8990.00y13019090.0y13058722.0-100.00268.00-0.0111230.0298112013-01-04 00:00:00
4P6312.50p13016258.0p13057000.054.50-687.500.008634-0.1089112013-01-04 00:00:00
5L11216.67l130111370.0l130510880.0-153.33336.67-0.0136700.0300152013-01-04 00:00:00
6V6486.57v13016355.0v13056685.0131.57-198.430.020283-0.0305912013-01-04 00:00:00
7PM2503.33pm3012520.0pm3092498.0-16.675.33-0.0066590.0021292013-01-04 00:00:00
8CF19158.25cf30120000.0cf30519005.0-841.75153.25-0.0439370.0079992013-01-04 00:00:00
9SR5984.00SR3015659.0SR3055567.0325.00417.000.0543110.0696862013-01-04 00:00:00
varietyspot_pricenear_contractnear_contract_pricedominant_contractdominant_contract_pricenear_basisdom_basisnear_basis_ratedom_basis_ratedate
105977WR3833.20wr24034230.0wr24053898.0-396.80-64.80-0.103517-0.0169052024-03-11 00:00:00
105978HC3888.00hc24033826.0hc24053798.062.0090.000.0159470.0231482024-03-11 00:00:00
105979FU5612.00fu24043200.0fu24053178.02412.002434.000.4297930.4337132024-03-11 00:00:00
105980BU3569.43bu24033558.0bu24063620.011.43-50.570.003202-0.0141682024-03-11 00:00:00
105981RU13210.00ru240314145.0ru240514180.0-935.00-970.00-0.070780-0.0734292024-03-11 00:00:00
105982SP6070.00sp24035946.0sp24056002.0124.0068.000.0204280.0112032024-03-11 00:00:00
105983SS13967.50ss240313645.0ss240513770.0322.50197.500.0230890.0141402024-03-11 00:00:00
105984BR13240.00br240313280.0br240413295.0-40.00-55.00-0.003021-0.0041542024-03-11 00:00:00
105985SI14940.00SI240313155.0SI240513270.01785.001670.000.1194780.1117802024-03-11 00:00:00
105986LC111400.00LC2403111100.0LC2407115100.0300.00-3700.000.002693-0.0332142024-03-11 00:00:00